knitr::opts_chunk$set(echo = TRUE)
In this exercise, you will load a filtered gapminder
dataset - with a subset of data on global development from 1952 - 2007
in increments of 5 years - to capture the period between the Second
World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks within this script.
First, start with installing and activating the relevant packages
tidyverse, gganimate, and
gapminder if you do not have them already. Pay
attention to what warning messages you get when installing
gganimate, as your computer might need other packages than
gifski and av
# install.packages("gganimate")
# install.packages("gifski")
# install.packages("av")
# install.packages("gapminder")
library(tidyverse)
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library(gganimate)
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library(gifski)
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library(av)
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library(gapminder)
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First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
ggtitle("Figure 01")
…
We see an interesting spread with an outlier to the right. Explore who it is so you can answer question 2 below!
I first use “view(gapminder)” to view the dataset. Here I can filter for highest gdpPercap, which shows Kuwait, but does not immediately confirm that it was also so in 1952. Thus, I use a filter command to view the highest gpd nation in 1952: “> gapminder %>% + filter(year == 1952) %>% + arrange(desc(gdpPercap)) %>% + slice(1)” Which confirms that it is Kuwait, also in 1952.
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
ggtitle("Figure 02")
…
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Answer: why does it make sense to have a log10 scale
(scale_x_log10()) on the x axis? (hint: try to comment
it out and observe the result) It makes sense, since the x-axis is
gdpPercap, which has huge variance, and therefore the log10 scale allows
us to see the multitude of levels of national gross domestic product. If
it were a normal linear scale, the nations with a large GDP would
seemingly fly off the charts, while the low-GDP nations be barely
visible at the other end. This log10 scale allows us to fairly easily
see all the nations, even though their GDP is very varied.
Answer: In Figure 1: Who is the outlier (the richest country in 1952) far right on the x axis?
Kuwait, see answer/solution to how I saw this above.
Fig 1 Fixed:
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop, color = continent)) + geom_point() + scale_x_log10(labels = scales::comma) + labs (x = "GDP per capita (USD)", y = "Life Expentancy (years)") + ggtitle("Figure 01A 1952 Life Expectancy and GDP")
Fig 2 Fixed:
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10(labels = scales::comma) + labs(x = "GDP per capita (USD)", y = "Life Expectancy (years)") +
ggtitle("Figure 02A 2007 Life Expectancy and GDP")
top_5_gdpPercap <- gapminder %>%
filter(year == 2007) %>%
arrange(desc(gdpPercap)) %>% # Sort by GDP per capita in descending order
slice(1:5) # Select top 5
print(top_5_gdpPercap)
## # A tibble: 5 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Norway Europe 2007 80.2 4627926 49357.
## 2 Kuwait Asia 2007 77.6 2505559 47307.
## 3 Singapore Asia 2007 80.0 4553009 47143.
## 4 United States Americas 2007 78.2 301139947 42952.
## 5 Ireland Europe 2007 78.9 4109086 40676.
This tibble shows the five nations with the highest GDP per capita in 2007.
The comparison would be easier if we had the two graphs together,
animated. We have a lovely tool in R to do this: the
gganimate package. Beware that there may be other packages
your operating system needs in order to glue interim images into an
animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
…
This plot collates all the points across time. The next step is to
split it into years and animate it. This may take some time, depending
on the processing power of your computer (and other things you are
asking it to do). Beware that the animation might appear in the bottom
right ‘Viewer’ pane, not in this rmd preview. You need to
knit the document to get the visual inside an html
file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smooths the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may
need to troubleshoot your installation of gganimate and
other packages
transition_states() and transition_time()
functions respectively)Here adding a year-title to animation number 2:
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point(alpha = 0.7) +
scale_x_log10() +
labs(
x = "GDP per capita (log scale, USD)",
y = "Life Expectancy (years)"
) +
ggtitle("Year: {frame_time}") +
transition_time(year)
anim2
Her hjalp ChatGPT en del! Vigtigt at huske at loade “scales” package for at ændre scientific notation.
library(scales)
## Warning: pakke 'scales' blev bygget under R version 4.4.3
##
## Vedhæfter pakke: 'scales'
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##
## discard
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## col_factor
anim2 <- ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) +
geom_point(alpha = 0.7) +
scale_x_log10(labels = scales::comma) + # Converts large numbers into readable format
scale_y_continuous(labels = scales::comma) + # Ensures y-axis uses whole numbers
scale_size(range = c(2, 10), guide = "none") + # Keeps size but removes population labels
scale_color_manual(values = c(
"Africa" = "red", "Americas" = "blue", "Asia" = "green",
"Europe" = "purple", "Oceania" = "orange"
)) + # Differentiates continents by color
labs(
title = "Global Development Over Time: {frame_time}",
x = "GDP per Capita (USD, log scale)",
y = "Life Expectancy (Years)",
color = "Continent"
) +
theme_minimal(base_size = 14) + # Improve readability
theme(
legend.position = "bottom", # Move legend below for better visibility
axis.text.x = element_text(angle = 45, hjust = 1) # Rotates x-axis labels for clarity
) +
transition_time(year) # Animates over time using the year variable
anim2
gapminder_unfiltered dataset or
download more historical data at https://www.gapminder.org/data/ ]I use “sort(unique(gapminder$year))” to view the years in the dataset. Luckily, there is data from the year 1997; when I was born. Thus it will be pretty easy to compare my birthyear to 10 years later, 2007, where we also have data from. I will compare the two years 1997 and 2007 in terms of life expectancy.
I will define ‘better’ in terms of life expectancy. I make a simple boxplot comparison by the help of ChatGPT. In this boxplot we see the median line having been raised above 70 years, which means that half of the worlds population in 2007 lives for longer than 70 years, compared with the median line slightly below 70 in 1997, which means that half of the worlds population lived for longer than circa 68 years in 1997. This comparison shows a marked improvement in the average life expectancy in the world across the short time from 1997 to 2007.
library(ggplot2)
library(dplyr)
# Filter dataset for 1997 and 2007 only
gapminder_filtered <- gapminder %>%
filter(year %in% c(1997, 2007))
# Create the boxplot
ggplot(gapminder_filtered, aes(x = factor(year), y = lifeExp, fill = factor(year))) +
geom_boxplot(alpha = 0.7) +
scale_fill_manual(values = c("1997" = "blue", "2007" = "red")) + # Different colors for years
labs(
title = "Comparison of Life Expectancy in 1997 and 2007",
x = "Year",
y = "Life Expectancy (Years)",
fill = "Year"
) +
theme_minimal(base_size = 14)